scholarly journals Multiple optimal phenotypes overcome redox and glycolytic intermediate metabolite imbalances inEscherichia coli pgiknockout evolutions

2018 ◽  
Author(s):  
Douglas McCloskey ◽  
Sibei Xu ◽  
Troy E. Sandberg ◽  
Elizabeth Brunk ◽  
Ying Hefner ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Gene Product ◽  
Major Gene ◽  
Regulatory Function ◽  
Second Step ◽  
Valuable Insight ◽  
Adaptive Laboratory Evolution ◽  
Laboratory Evolution ◽  
K 12 ◽  
Mg1655 Strain

AbstractA mechanistic understanding of how new phenotypes develop to overcome the loss of a gene product provides valuable insight on both the metabolic and regulatory function of the lost gene. Thepgigene, whose product catalyzes the second step in glycolysis, was deleted in a growth optimizedEscherichia coliK-12 MG1655 strain. The knock-out (KO) strain exhibited an 80% drop in growth rate, that was largely recovered in eight replicate, but phenotypically distinct, cultures after undergoing adaptive laboratory evolution (ALE). Multi omic data sets showed that the loss ofpgisubstantially shifted pathway usage leading to a redox and sugar phosphate stress response. These stress responses were overcome by unique combinations of innovative mutations selected for by ALE. Thus, we show the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after loss of a major gene product.ImportanceA mechanistic understanding of how new phenotypes develop to overcome the loss of a gene product provides valuable insight on both the metabolic and regulatory function of the lost gene. Thepgigene, whose product catalyzes the second step in glycolysis, was deleted in a growth optimizedEscherichia coliK-12 MG1655 strain. Eight replicate adaptive laboratory evolution (ALE) resulted in eight phenotypically distinct endpoints that were able to overcome the gene loss. Utilizing multi-omics analysis, we show the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after loss of a major gene product.

scholarly journals Multiple Optimal Phenotypes Overcome Redox and Glycolytic Intermediate Metabolite Imbalances inEscherichia coli pgiKnockout Evolutions

2018 ◽  
Vol 84 (19) ◽  
Author(s):  
Douglas McCloskey ◽  
Sibei Xu ◽  
Troy E. Sandberg ◽  
Elizabeth Brunk ◽  
Ying Hefner ◽  
...  
Keyword(s):  
Gene Product ◽  
Stress Responses ◽  
Major Gene ◽  
Valuable Insight ◽  
Adaptive Laboratory Evolution ◽  
Glycolytic Intermediate ◽  
Laboratory Evolution ◽  
Content Type ◽  
Mechanistic Understanding ◽  
K 12

ABSTRACTA mechanistic understanding of how new phenotypes develop to overcome the loss of a gene product provides valuable insight on both the metabolic and regulatory functions of the lost gene. Thepgigene, whose product catalyzes the second step in glycolysis, was deleted in a growth-optimizedEscherichia coliK-12 MG1655 strain. The initial knockout (KO) strain exhibited an 80% drop in growth rate that was largely recovered in eight replicate, but phenotypically distinct, cultures after undergoing adaptive laboratory evolution (ALE). Multi-omic data sets showed that the loss ofpgisubstantially shifted pathway usage, leading to a redox and sugar phosphate stress response. These stress responses were overcome by unique combinations of innovative mutations selected for by ALE. Thus, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.IMPORTANCEA mechanistic understanding of how microbes are able to overcome the loss of a gene through regulatory and metabolic changes is not well understood. Eight independent adaptive laboratory evolution (ALE) experiments withpgiknockout strains resulted in eight phenotypically distinct endpoints that were able to overcome the gene loss. Utilizing multi-omics analysis, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.

scholarly journals Use of Adaptive Laboratory Evolution To Discover Key Mutations Enabling Rapid Growth of Escherichia coli K-12 MG1655 on Glucose Minimal Medium

2014 ◽  
Vol 81 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Ryan A. LaCroix ◽  
Troy E. Sandberg ◽  
Edward J. O'Brien ◽  
Jose Utrilla ◽  
Ali Ebrahim ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Rapid Growth ◽  
Scientific Discovery ◽  
Maximum Growth ◽  
Time Frame ◽  
Adaptive Laboratory Evolution ◽  
Genetic Changes ◽  
Laboratory Evolution ◽  
Content Type ◽  
K 12

ABSTRACTAdaptive laboratory evolution (ALE) has emerged as an effective tool for scientific discovery and addressing biotechnological needs. Much of ALE's utility is derived from reproducibly obtained fitness increases. Identifying causal genetic changes and their combinatorial effects is challenging and time-consuming. Understanding how these genetic changes enable increased fitness can be difficult. A series of approaches that address these challenges was developed and demonstrated usingEscherichia coliK-12 MG1655 on glucose minimal media at 37°C. By keepingE. coliin constant substrate excess and exponential growth, fitness increases up to 1.6-fold were obtained compared to the wild type. These increases are comparable to previously reported maximum growth rates in similar conditions but were obtained over a shorter time frame. Across the eight replicate ALE experiments performed, causal mutations were identified using three approaches: identifying mutations in the same gene/region across replicate experiments, sequencing strains before and after computationally determined fitness jumps, and allelic replacement coupled with targeted ALE of reconstructed strains. Three genetic regions were most often mutated: the global transcription generpoB, an 82-bp deletion between the metabolicpyrEgene andrph, and an IS element between the DNA structural genehnsandtdk. Model-derived classification of gene expression revealed a number of processes important for increased growth that were missed using a gene classification system alone. The methods described here represent a powerful combination of technologies to increase the speed and efficiency of ALE studies. The identified mutations can be examined as genetic parts for increasing growth rate in a desired strain and for understanding rapid growth phenotypes.

scholarly journals Adaptive laboratory evolution for improved tolerance of isobutyl acetate in Escherichia coli

2021 ◽  
Author(s):  
Morgan M. Matson ◽  
Mateo M. Cepeda ◽  
Angela Zhang ◽  
Anna E. Case ◽  
Erol S. Kavvas ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Adaptive Laboratory Evolution ◽  
Laboratory Evolution ◽  
Isobutyl Acetate

scholarly journals Improving the Methanol Tolerance of an Escherichia coli Methylotroph via Adaptive Laboratory Evolution Enhances Synthetic Methanol Utilization

2021 ◽  
Vol 12 ◽  
Author(s):  
R. Kyle Bennett ◽  
Gwendolyn J. Gregory ◽  
Jacqueline E. Gonzalez ◽  
Jie Ren Gerald Har ◽  
Maciek R. Antoniewicz ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Ribosomal Subunit ◽  
Biological Properties ◽  
Methanol Tolerance ◽  
Adaptive Laboratory Evolution ◽  
Laboratory Evolution ◽  
E Coli ◽  
Translational Accuracy ◽  
Methanol Utilization ◽  
Carbon Compounds

There is great interest in developing synthetic methylotrophs that harbor methane and methanol utilization pathways in heterologous hosts such as Escherichia coli for industrial bioconversion of one-carbon compounds. While there are recent reports that describe the successful engineering of synthetic methylotrophs, additional efforts are required to achieve the robust methylotrophic phenotypes required for industrial realization. Here, we address an important issue of synthetic methylotrophy in E. coli: methanol toxicity. Both methanol, and its oxidation product, formaldehyde, are cytotoxic to cells. Methanol alters the fluidity and biological properties of cellular membranes while formaldehyde reacts readily with proteins and nucleic acids. Thus, efforts to enhance the methanol tolerance of synthetic methylotrophs are important. Here, adaptive laboratory evolution was performed to improve the methanol tolerance of several E. coli strains, both methylotrophic and non-methylotrophic. Serial batch passaging in rich medium containing toxic methanol concentrations yielded clones exhibiting improved methanol tolerance. In several cases, these evolved clones exhibited a > 50% improvement in growth rate and biomass yield in the presence of high methanol concentrations compared to the respective parental strains. Importantly, one evolved clone exhibited a two to threefold improvement in the methanol utilization phenotype, as determined via 13C-labeling, at non-toxic, industrially relevant methanol concentrations compared to the respective parental strain. Whole genome sequencing was performed to identify causative mutations contributing to methanol tolerance. Common mutations were identified in 30S ribosomal subunit proteins, which increased translational accuracy and provided insight into a novel methanol tolerance mechanism. This study addresses an important issue of synthetic methylotrophy in E. coli and provides insight as to how methanol toxicity can be alleviated via enhancing methanol tolerance. Coupled improvement of methanol tolerance and synthetic methanol utilization is an important advancement for the field of synthetic methylotrophy.

scholarly journals Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

2021 ◽  
Author(s):  
Patrick Victor Phaneuf ◽  
Daniel Craig Zielinski ◽  
James T Yurkovich ◽  
Josefin Johnsen ◽  
Richard Szubin ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Isobutyric Acid ◽  
Data Driven ◽  
Adaptive Laboratory Evolution ◽  
Laboratory Evolution ◽  
Strain Design ◽  
Analysis Workflow ◽  
High Level ◽  
K 12 ◽  
Entire Dataset

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological system. Adaptive Laboratory Evolution (ALE) leverages nature's problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study presents a novel and first of its kind meta-analysis workflow to derive data-driven strain designs from aggregate ALE mutational data using rich mutation annotations, statistical and structural biology methods. The mutational dataset consolidated and utilized in this study contained 63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13,957 observed mutations. High-level trends across the entire dataset were established and revealed that ALE-derived strain designs will largely be gene-centric, as opposed to non-coding, and a relatively small number of variants (approx. 4) can significantly alter cellular states and provide benefits which range from an increase in fitness to a complete necessity for survival. Three novel experimentally validated designs relevant to metabolic engineering applications are presented as use cases for the workflow. Specifically, these designs increased growth rates with glycerol as a carbon source through a point mutation to glpK and a truncation to cyaA or increased tolerance to toxic levels of isobutyric acid through a pykF truncation. These results demonstrate how strain designs can be extracted from aggregated ALE data to enhance strain design efforts.

scholarly journals Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

2021 ◽  
Author(s):  
Patrick V. Phaneuf ◽  
Daniel C. Zielinski ◽  
James T. Yurkovich ◽  
Josefin Johnsen ◽  
Richard Szubin ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Data Driven ◽  
Adaptive Laboratory Evolution ◽  
Laboratory Evolution ◽  
Strain Design
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